Lindahl, Mikael

KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).

2016 (English)Doctoral thesis, comprehensive summary (Other academic)

Abstract [en]

The basal ganglia (BG), a group of nuclei in the forebrain of all vertebrates, are important for behavioral selection. BG receive contextual input from most cortical areas as well as from parts of the thalamus, and provide output to brain systems that are involved in the generation of behavior, i.e. the thalamus and the brain stem. Many neurological disorders such as Parkinson’s disease and Huntington’s disease, and several neuropsychiatric disorders, are related to BG. Studying BG enhances the understanding as to how behaviors are learned and modified. These insights can be used to improve treatments for several BG disorders, and to develop brain-inspired algorithms for solving special information-processing tasks.

In this thesis modeling and simulations have been used to investigate function and dynamics of BG. In the first project a model was developed to explore a new hypothesis about how conflicts between competing actions are resolved in BG. It was proposed that a subsystem named the arbitration system, composed of the subthalamic nucleus (STN), pedunculopontine nucleus (PPN), the brain stem, central medial nucleus of thalamus (CM), globus pallidus interna (GPi) and globus pallidus externa (GPe), resolve basic conflicts between alternative motor programs. On top of the arbitration system there is a second subsystem named the extension systems, which involves the direct and indirect pathway of the striatum. This system can modify the output of the arbitration system to bias action selection towards outcomes dependent on contextual information.

In the second project a model framework was developed in two steps, with the aim to gain a deeper understanding of how synapse dynamics, connectivity and neural excitability in the BG relate to function and dynamics in health and disease. First a spiking model of STN, GPe and substantia nigra pars reticulata (SNr), with emulated inputs from striatal medium spiny neurons (MSNs) and the cortex, was built and used to study how synaptic short-term plasticity affected action selection signaling in the direct-, hyperdirect- and indirect pathways. It was found that the functional consequences of facilitatory synapses onto SNr neurons are substantial, and only a few presynaptic MSNs can suppress postsynaptic SNr neurons. The model also predicted that STN signaling in SNr is mainly effective in a transient manner. The model was later extended with a striatal network, containing MSNs and fast spiking interneurons (FSNs), and modified to represent GPe with two types of neurons: type I, which projects downstream in BG, and type A, which have a back-projection to striatum. Furthermore, dopamine depletion dependent modification of connectivity and neuron excitability were added to the model. Using this extended BG model, it was found that FSNs and GPe type A neurons controlled excitability of striatal neurons during low cortical drive, whereas MSN collaterals have a greater impact at higher cortical drive. The indirect pathway increased the dynamical range over which two possible action commands were competing, while removing intrastriatal inhibition deteriorated action selection capabilities. Dopamine-depletion induced effects on spike synchronization and oscillations in the BG were also investigated here.

For the final project, an abstract spiking BG model which included a hypothesized control of the reward signaling dopamine system was developed. This model incorporated dopamine-dependent synaptic plasticity, and used a plasticity rule based on probabilistic inference called Bayesian Confidence Propagation Neural Network (BCPNN). In this paradigm synaptic connections were controlled by gathering statistics about neural input and output activity. Synaptic weights were inferred using Bayes’ rule to estimate the confidence of future observations from the input. The model exhibits successful performance, measured as a moving average of correct selected actions, in a multiple-choice learning task with a changeable reward schedule. Furthermore, the model predicts a decreased performance upon dopamine lesioning, and suggests that removing the indirect pathway may disrupt learning in profound ways.

Place, publisher, year, edition, pages

Stockholm: Kungliga Tekniska högskolan, 2016. p. 47

Series

TRITA-CSC-A, ISSN 1653-5723 ; 1653-5723

National Category

Neurosciences

Research subject

Computer Science

Identifiers

urn:nbn:se:kth:diva-185160 (URN)978-91-7595-925-2 (ISBN)

Public defence

2016-05-04, FS, Lindstedtsvägen 26, KTH Campus, Stockholm, 13:00

Opponent

Humphries, Mark

University of Manchester.

Supervisors

Hällgren Kotaleski, Jeanette

KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).

Tully, Philip

KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). University of Edinburgh School of Informatics.

2017 (English)Doctoral thesis, comprehensive summary (Other academic)

Abstract [en]

Cortical and subcortical microcircuits are continuously modified throughout life. Despite ongoing changes these networks stubbornly maintain their functions, which persist although destabilizing synaptic and nonsynaptic mechanisms should ostensibly propel them towards runaway excitation or quiescence. What dynamical phenomena exist to act together to balance such learning with information processing? What types of activity patterns

do they underpin, and how do these patterns relate to our perceptual experiences? What enables learning and memory operations to occur despite such massive and constant neural reorganization? Progress towards answering many of these questions can be pursued through large-scale neuronal simulations.

The thesis seeks to demonstrate how multiple interacting plasticity mechanisms can coordinate reinforcement, auto- and hetero-associative learning within large-scale, spiking, plastic neuronal networks. Spiking neural networks can represent information in the form of probability distributions, and a biophysical realization of Bayesian computation can help reconcile disparate experimental observations.